sc-PDB:  an Annotated Database of Druggable Binding Sites from the Protein Data Bank

Abstract

The sc-PDB is a collection of 6 415 three-dimensional structures of binding sites found in the Protein Data Bank (PDB). Binding sites were extracted from all high-resolution crystal structures in which a complex between a protein cavity and a small-molecular-weight ligand could be identified. Importantly, ligands are considered from a pharmacological and not a structural point of view. Therefore, solvents, detergents, and most metal ions are not stored in the sc-PDB. Ligands are classified into four main categories: nucleotides (< 4-mer), peptides (< 9-mer), cofactors, and organic compounds. The corresponding binding site is formed by all protein residues (including amino acids, cofactors, and important metal ions) with at least one atom within 6.5 angstroms of any ligand atom. The database was carefully annotated by browsing several protein databases (PDB, UniProt, and GO) and storing, for every sc-PDB entry, the following features: protein name, function, source, domain and mutations, ligand name, and structure. The repository of ligands has also been archived by diversity analysis of molecular scaffolds, and several chemoinformatics descriptors were computed to better understand the chemical space covered by stored ligands. The sc-PDB may be used for several purposes: (i) screening a collection of binding sites for predicting the most likely target(s) of any ligand, (ii) analyzing the molecular similarity between different cavities, and (iii) deriving rules that describe the relationship between ligand pharmacophoric points and active-site properties. The database is periodically updated and accessible on the web at http://bioinfo-pharma.u-strasbg.fr/scPDB/.

A simple representation of three-dimensional molecular structure

“…Whereas heroic effort has gone into solving the crystallographic conformations of hundreds of thousands of small molecules 85,86 , the bindingcompetent 3D conformations for millions of research 25 11, 2017; to prioritize which of these conformers may be most relevant for a given protein or question.…”

Comparative Modeling: The State of the Art and Protein Drug Target Structure Prediction

“…ChEMBL https://www.ebi.ac.uk/chembl DrugBank http://www.drugbank.ca [190] PDTD http://www.dddc.ac.cn/pdtd [191] PubChem http://pubchem.ncbi.nlm.nih.gov [192] sc-PDB http://bioinfo-pharma.u-strasbg.fr/scPDB [193] Binding Site Prediction 3DLigandSite http://www.sbg.bio.ic.ac.uk/~3dligandsite [194] MetaPocket http://metapocket.eml.org [195] PocketDepth http://proline.physics.iisc.ernet.in/pocketdepth [196] Docking Tools SPROUT http://www.simbiosys.ca/sprout [206] modeling as it complicates the sequence-structure relationship and may partially account for the moderate performance of existing GPCR templates. Nonetheless, these newly solved structures of CXCR4 introduce valuable information to GPCR modeling, which is heavily reliant on existing structural knowledge of GPCRs.…”

In silico structure‐activity‐relationship (SAR) models from machine learning: a review

“…Since then, there have been a number of Chemogenomics efforts that have primarily focused on kinases [Vieth et al, 2004;Hu et al, 2005;Birault et al, 2006;Kellenberger et al, 2006;Hoppe et al, 2006], and GPCRs [Jacoby et al, 1999;Jacoby, 2001;Frimurer et al, 2005;Surgand et al, 2006]. Some of these approaches identify the right subset of family members using similarity search, either with respect to sequence [Frimurer et al, 2005;Surgand et al, 2006] or structure [Hu et al, 2005;Kellenberger et al, 2006;Hoppe et al, 2006], whereas other approaches employ machine-learning techniques to estimate and analyze the ligand-target affinity within each family Gough, 2002, 2005;Vieth et al, 2004;Jacob and Vert, 2008]. Even though chemogenomics-based approaches have been successfully used to identify lead compounds Eguchi et al, 2003;Klabunde and Jger, 2006;Martin et al, 2007], the methods that were developed are to a large extent specific to kinases and GPCRs and have a significant manual component.…”